Ex-Tesla Team Raises $12.5M to Put Supply Chains on Autopilot

Supply-chain startup Atomic raises $12.5M Series A to automate inventory planning, operational decision-making A startup that helps companies with complex supply chains run their inventory and operations will use the funds to expand its AI platform.

The Boston-based company was created by Tesla alumni Michael Rossiter and Neal Suidan, who initially invented an early version of the technology as Tesla employees during the production ramp for the Model 3 in 2018. The system was designed to assist in the company's supply-planning problems, which couldn't be addressed by traditional spreadsheets, as the operation became increasingly complex.

Atomic burst out of stealth last year with plans to bring this model to other companies. Its software models various supply-chain situations, calculates how much inventory organizations should have and where it should be stored, and suggests what to do next. The company's platform has been upgraded to automate some of those recommendations.

Atomic says its annual recurring revenue has grown five times since the start of 2026. Its customer list now includes Door Dash and Hello Fresh, as its software is used to inform purchasing and inventory decisions for multiple sites. Door Dash, which has a stake in Atomic, uses the platform for 90% of its purchases for hundreds of locations, according to Jon McNeill, a former Tesla president who is on Atomic's board.

The fresh round of investment was led by Klass Capital and Madrona Venture Group, which hiked Atomic's Series A funding to more than $15 million. The company revealed Jeff Goodrich, a veteran Tesla planning director, as its new CTO and third co-founder.

"Atomic believes that its technology can help companies waste less and spoil more - especially in sectors in which fast-depreciating inventory is common," the company noted. It's also moving into consumer packaged goods, mobility and manufacturing - where supply-chain planning includes alternate constraints.

For Atomic, one of the biggest areas of concern is lowering the length of time it takes to learn a new customer. "We've built models that use AI to figure out what are the decision rules that people may or may not have written down but we can then use that to shift the model from 'you should do this' to one that can actually make a decision based on the inputs within some boundary conditions," he explained.

The funding reflects an increased desire among investors to fund agentic AI and application tools that can perform operational work rather than output just text. At Atomic, the idea is to shift away from spreadsheet-laden supply-chain planning that only reacts to shifting circumstances.